title: “assignment 8” output: html_document output_file: “C:\Users\student\OneDrive - Bryant University\Documents\GitHub\Math421\assignment8.html” knit: (function(inputFile, encoding) { options(repos = c(CRAN = “https://cran.rstudio.com/”)) rmarkdown::render(inputFile, encoding = encoding) }) —

---

How to do it?:

• Open the Rmarkdown file of this assignment (link) in Rstudio.

• Right under each question, insert a code chunk (you can use the hotkey Ctrl + Alt + I to add a code chunk) and code the solution for the question.

• Knit the rmarkdown file (hotkey: Ctrl + Alt + K) to export an html.

• Publish the html file to your Githiub Page.

Submission: Submit the link on Github of the assignment to Canvas

---

1. Install two packages gganimate and gifski then restart Rstudio. Using the Adult Census Income data, make an animation using geom_point and transition_states.

getwd()

## [1] "C:/Users/student/Downloads"

library(tidyverse)
library(gganimate)
library(gifski)
library(ggplot2)
library(knitr)
df = read_csv("adult_census_missing.csv")
plot1 = ggplot(df, 
       aes(x = education.num, 
           y = hours.per.week))+
  geom_point(size=4) + 
  transition_states(income)+
labs(title = 'income: {closest_state}')
animate(plot1, nframes = 400)

2. Using the Adult Census Income data, make an animation using geom_bar and transition_states.

library(tidyverse)
library(gganimate)
library(gifski)
library(knitr)
library(dplyr)
df = read_csv("adult_census_missing.csv")
df_clean <- df %>% filter(!is.na(sex))

plot2 <- df_clean %>%
ggplot(aes(x = sex, fill = income)) +
geom_bar(position = "fill") +
transition_states(education.num) +
labs(title = "education.num: {closest_state}")

animate(plot2, nframes = 400)

3. Use the WHO’s dataset at this link. Make a top-10 bar race by months between countries on the number of deaths by Covid 19 in 2021. library(tidyverse) library(gganimate) library(gifski) library(knitr) library(lubridate) library(dplyr) library(lubridate) library(dplyr)

setwd("C:\\Users\\student\\OneDrive - Bryant University\\Documents\\GitHub\\Math421")
df2 <- read_csv("WHO-COVID-19-global-data.csv")
library(lubridate)
df2$Date_reported <- as.Date(df2$Date_reported)
df2$months <- month(df2$Date_reported)
d1 <- df2 %>% group_by(months, Country) %>% summarise(mean = mean(Cumulative_deaths))
d2 <- d1 %>% group_by(months) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)
plot3 <- d3 %>% 
  ggplot(aes(x=rank, y=mean, group=Country, fill=Country)) +
  geom_col() +
  geom_text(aes(label=Country), hjust=1.4) +
  coord_flip(clip="off", expand=FALSE) +
  scale_x_reverse() +
  labs(title='Month: {closest_state}', x='', y='Cumulative Deaths') +
  theme(plot.title = element_text(hjust=1, size=22),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) +
  transition_states(months)

animate(plot3, nframes = 400)

4. Make a bar race using a dataset of your own interest. You may use the dataset that we use in class (https://covidtracking.com/data/download/all-states-history.csv) but you should make a different bar racev from ones in the slides. library(tidyverse) library(gganimate) library(gifski) library(knitr) library(lubridate) library(dplyr) library(lubridate) library(dplyr)

setwd("C:\\Users\\student\\OneDrive - Bryant University\\Documents\\GitHub\\Math421")
df3 = read.csv('all-states-history.csv') 
df3 <- df3 %>% filter(deathIncrease>0, positiveIncrease>0) 
df3$date = as.Date(df3$date, format = "%m/%d/%Y") 
df3$year <- year(df3$date) 
d1 <- df3 %>% group_by(year, state) %>% summarise(mean = mean(positive)) 
d2 <- d1 %>% group_by(year) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10) 
plot4 <- d3 %>% ggplot(aes(x=rank, y=mean, group=state, fill=state, label=state)) + geom_col()+ 
  geom_text(aes(y = mean, label = state), hjust = 1.4)+ 
  coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+ 
  labs( 
    title = 'Year: {closest_state}', x = '',
    y = 'Total Number of Positive Cases', 
    fill = 'state' 
    ) + 
    theme(plot.title = element_text(hjust = 1, size = 22), 
          axis.ticks.y = element_blank(), 
          axis.text.y = element_blank()) + transition_states(year)+ 
          ease_aes("cubic-in-out")
    animate(plot4, nframes = 400)